Demand for healthcare services is growing rapidly in Australia, and rising healthcare expenditure is increasing pressure on the sustainability of the government-funded healthcare system. To keep up with the rising demand, we need to be more efficient in delivering healthcare services. To make a system efficient, we need to identify the source of inefficiency and eliminate it.
In this research project, we will apply statistical and operations research tools to improve the healthcare delivery process in the surgical suite of a major metropolitan hospital. Randomness in patients’ length of stay (LoS) at healthcare facilities is the major cause of inefficiency in the system. We will develop a scientific method to minimise inefficiency in the system because of the randomness in patient's LoS.
We will develop some strategies to understand the distribution of healthcare LoS data. Understanding the healthcare data is important in order to characterise the load each patient brings to the system. Next, we will develop a patient classification scheme to classify the elective surgery patients into lower variability LoS groups. Doing so will help us in decreasing the variability caused by stochastic LoS.
We will develop a mixed integer linear programming (MIP) based elective surgery scheduling scheme to maximise the throughput in a surgical suite. We will test our scheduling scheme by using a simulation model and will analyse patient flow after implementing the new scheduling scheme. We will also develop an assessment tool to predict the availability of resources depending on the current resource users.